Teaching artificial intelligence (AI) is an emerging challenge in global school education. There are considerable barriers to overcome, including the existing practices of technology education and teachers’ knowledge of AI. Research evidence shows that studying teachers’ experiences can be beneficial in informing how appropriate design in teaching sustainable AI should evolve. Design frames characterize teachers’ design reasoning and can substantially influence their AI lesson design considerations. This study examined 18 experienced teachers’ perceptions of teaching AI and identified effective designs to support AI instruction. Data collection methods involved semi-structured interviews, action study, classroom observation, and post-lesson discussions with the purpose of analyzing the teachers’ perceptions of teaching AI. Grounded theory was employed to detail how teachers understand the pedagogical challenges of teaching AI and the emerging pedagogical solutions from their perspectives. Results reveal that effective AI instructional design should encompass five important components: (1) obstacles to and facilitators of participation in teaching AI, (2) interactive design thinking processes, (3) teachers’ knowledge of teaching AI, (4) orienteering AI knowledge for social good, and (5) the holistic understanding of teaching AI. The implications for future teacher AI professional development activities are proposed.

Close links between students' conceptions of and approaches to learning were established in the past research. However, only a few quantitative studies investigated this relationship particularly with regard to mobile learning (m‐learning). The correlation between learners' conceptions and approaches to m‐learning was analysed using a partial least squares analysis applied to data obtained from a sample of 971 undergraduate students in China. The results indicated that students' conceptions of m‐learning could be classified into reproductive, transitional, and constructive levels. Students may hold multiple m‐learning applications than a predominant one; hence, examining m‐learning as one monolithic entity may provide limited information. Latent profile analysis identified four learning profiles based on students' preferred m‐learning applications: passive, mixed, surface‐supportive, and high‐engagement.. Moreover, a general trend was observed, whereby students with reproductive and surface‐supportive learning profiles showed a tendency to adopt surface approaches, whereas those expressing constructive and mixed learning profiles were more inclined to adopt deep approaches. Interestingly, students with transitional conceptions and high‐engagement learning profiles tended to take both surface and deep approaches.

With the increasing importance of adult and continuing education, the present study aimed to examine the factors that influence continuing web-based learning at work. Three questionnaires were utilised to investigate the association of the job characteristics from Karasek et al.’s (1998) job demand-control-support model and the self-regulated learning with web-based continuing learning; exploratory factor analyses indicate their adequate reliability and validity. A sample of 203 employees of an airline company completed three questionnaires. The path analysis reveals that job demands did not have any significant correlation with any other variables. However, job control, social support and self-regulated learning constituted significant predictors of attitudes towards web-based continuing learning. Furthermore, self-regulated learning mediated job characteristics and attitudes. In conclusion, this is one of the few studies to consider perceptions of both personal online learning (i.e., self-regulated learning and attitudes towards web-based continuing learning) and work-related variables (i.e., job characteristics). The study advances interdisciplinary perspectives on education, information and communications technology and psychology, which has implications for continuing adult education and successful implementation of online workplace learning.